According to U.S. Environmental Protection Agency, buildings account for 36 percent of total energy use and 65 percent of electricity consumption in the United States. Hundreds of billions in energy savings may be achieved if energy consumption and energy demand of each building can be optimized.
However, energy use patterns of a specific building can be complex and may change over years. Energy use patterns of a typical building may be affected by factors such as weather, the age of the building, building equipment (e.g., air-conditioning systems, heating ventilation systems, lighting systems and windows), occupancy levels, occupancy hours, building control systems, operator or occupant behavior, the characteristics of the building roof, walls and floor, the building function, etc.
On the other hand, digital energy and non-energy information of many buildings, such as energy interval data commonly associated with smart meters, local historical weather data, operational data regarding the state of equipment such as lights, windows, and air-conditioning, occupancy data, etc. are readily available.
In consequence, the present inventors have recognized that there is value and need in providing a method for energy management by analyzing energy data in conjunction with non-energy data of a specific building and optimizing energy consumption, energy demand, and cost. Further, by performing such analysis on an ongoing basis or across a portfolio of buildings, the techniques developed can detect trends, changes, and outliers that may indicate fault conditions or opportunities for operational improvements. Finally, by combining the analytic output with detailed energy cost data and other decision procedures related to best practices in facilities operations, the techniques can be used to develop and refine a consolidated list of operational issues, each scored in terms of material relevance to the building owner.